Matlab Tutorial. Session 1 Basics, Filters, Color Space, Derivatives, Pyramids, Optical Flow

Introduction to mathematical programming

Matlab Tutorial.Session 1Basics, Filters, Color Space, Derivatives, Pyramids, Optical FlowGonzalo Vaca-CastanoBASICSMatlab Tutorial. Session 1Introduction to mathematical programmingCommonly used coding/computing environment in research: MATLAB (Matrix laboratory)

Ideal for numerical computationEasy to learnNo compiling hasslesQuick testing of ideas!Helpful tools and tricks for engineers/researchersExecution of tasks on command prompt

Matlab LayoutCurrent directoryBasic math operationsDMAS : / * + -

Exponents: ^

Trigonometric operations sin, asincos, acostan, atan

MatricesCreating a matrix: Example: >> [1 1; 0 1]

Matrix operationsAddition, multiplication, inverse, transpose + , * , inv() , VectorsCreating vectors: example: >> [1 2 3 4 5]

Vector operations:Dot product: .Sum all elements: sum()Sort elements: sort()Find histogram of elements: hist()Find average of elements: mean()Solving a system of linear equationsSolving a system of linear equationsa=[3 2 1; -1/2 2/3 -1; 1 -2 1]b=[0;1/2;-1]c=inv(a)*b;

Output: c= [-0.1429; 0.3214; -0.2143]

Processing images in MatlabImage2-D array of numbers (intensity values, gray levels)

Gray levels 0 (black) to 255 (white)

Color image is 3 2-D arrays of numbersRedGreenBlue11

12Example: Removing noise% Create a noisy imageI = imread('eight.tif');imshow(I)J = imnoise(I,'salt & pepper',0.02);figure, imshow(J)

% Mean filterK = filter2(fspecial('average',3),J)/255;figure, imshow(K)

%Median filterL = medfilt2(J,[3 3]);figure, imshow(L)Convolution

Color spaces: Rgb,HSV, etcMatlab Tutorial. Session 1

Image ArchitectureRaster Images (RGB, Gray Scale, Logical)

RGB(0 255)(0 255)(0 255)3 depths(0 255)Gray ScaleRowColSingle depthRow 1Logical (0 0r 1)Row 2Row 3(8-bit representation of colors)(8-bit representation of gray scale)(1-bit representation of black or white saturation)

RGB space>> I = imread('board.tif')

Displaying image:>> imshow(I)

Check image size>> size(I)

Convert color image to black and white image:>> rgb2gray(I)% Gray= 0.2989 * R + 0.5870 * G + 0.1140 * B Other Color spacesrgb2hsv(I)rgb2ycbcr(I)rgb2ntsc (I)

CIELAB or CIECAM02http://en.wikipedia.org/wiki/HSL_and_HSV

DerivativesDerivatives (Filters)In a continuos 1d Signal, derivative is:lim dx->0 f(x+dx)-f(x)/dxIn a discrete 1d signal, derivative is:f(x+1)-f(x)It is a convolution with the filter:

Other popular filter is:

Sobel FilterDerivatives in 2D

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[1 -1]KERNEL--[1 -2 1][1 -2 1] -- 0 1 0 1 -4 1 0 1 0Filtering in 2D Arrays (Convolution)

Alternative derivatives in 2D

Derivatives in MatlabI = imread('trees.tif');imshow(I)k1=[ 1 0 -1;2 0 -2; 1 0 -1]o=imfilter(double(I),k1,'same');figure; imagesc(o)colormap gray

QUESTION:Why imagesc instead of imshow ?PyramidsPyramidDefinition: is a type of multi-scale signal representation in which a signal or an image is subject to repeated smoothing and subsampling

Smoothing

L is a blurred image- G is the Gaussian Blur operator- I is an image- x,y are the location coordinates- is the scale parameter. Think of it as the amount of blur. Greater the value, greater the blur.- The * is the convolution operation in x and y. It applies gaussian blur G onto the image ISmoothing function% gauss_filter: Obtains a smoothed gaussian filter image% - Output: % smooth: image filter by a gaussian filter% - Input:% image: Matrix containing single band image.% sigma: Value of the sigma for the Gaussian filter% kernel_size: Size of the gaussian kernel (default: 6*sigma)function [smooth]= gauss_filter(image,sigma,kernel_size)if nargin < 2 sigma=1; endif nargin < 3 kernel_size=6*sigma;endgaussian_radio=floor(kernel_size/2); %radio of the gaussianx=[-gaussian_radio : gaussian_radio]; % x values (gaussian kernel size)gaussiano= exp(-x.^2/(2*sigma^2))/(sigma*sqrt(2*pi) ); %calculate the unidimensional gaussian temp=conv2(double(image),double(gaussiano),'same');smooth=conv2(double(temp),double(gaussiano'),'same');end

Example smoothingI = imread('eight.tif'); imagesc(I); colormap gray;antialiassigma=2;Ismooth=gauss_filter(I,antialiassigma,11);figure; imagesc(Ismooth); colormap gray;

SubsamplingI0 = imread('cameraman.tif');I1 = impyramid(I0, 'reduce');I2 = impyramid(I1, 'reduce');I3 = impyramid(I2, 'reduce'); imshow(I0)figure, imshow(I1)figure, imshow(I2)figure, imshow(I3)

See also: imresizeOptical FlowOptical flowDefinitionOptical floworoptic flowis the pattern of apparentmotionof objects, surfaces, and edges in a visual scene caused by the relative motion between an observer (aneyeor acamera) and the scene

Optical Flow code (Download it from webpage)i1=imread('view1.png');i2=imread('view5.png');[u,v,cert] =HierarchicalLK(rgb2gray(i1),rgb2gray(i2),3,2,2,1)flowtocolor(u,v)

Optical Flow Results